A Call Center System based on Expert Systems for the Acquisition of Agricultural Knowledge Transferred from Text-to-Speech in China

There is rich knowledge in expert systems that can be used to solve practical problems, but its promotion and application must rely on information facilities. The application of both computers and the Internet for Chinese farmers are not common, which leads to restrictions on the promotion and application of expert systems in rural areas of China. On the other hand, the existing call centers lack a professional knowledge base and the method of automatically calling the knowledge base in real-time, which makes it difficult to meet the needs of users wanting to obtain knowledge in a timely manner. To address these problems, a call center embedded in an expert system inference algorithm and knowledge base for farmers to obtain agricultural knowledge through mobile phones or fixed-line telephones was established. By studying the event-condition-action-based (ECA-based) database triggering model, remote method invocation-based (RMI-based) communication and iterative dichotomiser 3 algorithm-based (ID3-based) parameter extraction, the cohesion between the call center and the expert system was realized. The agricultural knowledge audio acquisition model was then coupled with the call center and the expert system was constructed, allowing farmers to acquire agricultural knowledge through mobile phones or fixed phones with fast responses. When used for cotton disease diagnosis, it can achieve a high diagnostic success rate (above 75%) when at least three disease symptoms are input into the expert system via the voice call, which provides an effective channel for Chinese farmers to obtain agricultural knowledge. It presents good application prospects in China, where 5G technology is currently developing rapidly.

[1]  Witold Pedrycz,et al.  Corrigendum to 'ECA Rule Learning in Dynamic Environments' Expert Systems with Applications, 41(17) (2014) 7847-7857] , 2015 .

[3]  Fredilyn B. Calanda,et al.  e-RICE: An Expert System using Rule-Based Algorithm to Detect, Diagnose, and Prescribe Control Options for Rice Plant Diseases in the Philippines , 2017 .

[4]  Ayman E. Khedr,et al.  Enhancing Iterative Dichotomiser 3 algorithm for classification decision tree , 2016, WIREs Data Mining Knowl. Discov..

[5]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[6]  Jessica Colnago,et al.  Choice of Voices: A Large-Scale Evaluation of Text-to-Speech Voice Quality for Long-Form Content , 2020, CHI.

[7]  Istas Pratomo,et al.  Expert system for diagnosis pests and diseases of the rice plant using forward chaining and certainty factor method , 2017, 2017 International Seminar on Intelligent Technology and Its Applications (ISITIA).

[8]  Adhistya Erna Permanasari,et al.  A Web-Based Decision Support System of Patient Time Prediction Using Iterative Dichotomiser 3 Algorithm , 2019, 2019 11th International Conference on Information Technology and Electrical Engineering (ICITEE).

[9]  Yu Zhang,et al.  A smart health service model for elders based on ECA-S rules , 2017, 2017 IEEE 15th International Conference on Software Engineering Research, Management and Applications (SERA).

[10]  John Mingers,et al.  An empirical comparison of selection measures for decision-tree induction , 2004, Machine Learning.

[11]  Pawel T. Wojciechowski,et al.  Atomic RMI: A Distributed Transactional Memory Framework , 2016, International Journal of Parallel Programming.

[12]  Trevor P Martin,et al.  A mass assignment based ID3 algorithm for decision tree induction , 1997 .

[13]  Nobuko Yoshida,et al.  Formalising Java RMI with explicit code mobility , 2007, Theor. Comput. Sci..

[14]  Gerhard P. Hancke,et al.  A Survey on 5G Networks for the Internet of Things: Communication Technologies and Challenges , 2018, IEEE Access.

[15]  R. Sugumar,et al.  Building a distributed K‐Means model for Weka using remote method invocation (RMI) feature of Java , 2019, Concurr. Comput. Pract. Exp..

[16]  Hyerim Bae,et al.  Automatic control of workflow processes using ECA rules , 2004, IEEE Transactions on Knowledge and Data Engineering.

[17]  Rafiqul Zaman Khan,et al.  Survey on Development of Expert System from 2010 to 2015 , 2016, ICTCS '16.

[18]  Cheng-Hong Yang,et al.  An Interactive Digital Somatosensory Game System That Implements a Decision Tree Algorithm , 2019, IEEE Access.

[19]  Shancang Li,et al.  5G Internet of Things: A survey , 2018, J. Ind. Inf. Integr..

[20]  Meriam Jemel,et al.  ECA rules for controlling authorisation plan to satisfy dynamic constraints , 2015, 2015 13th Annual Conference on Privacy, Security and Trust (PST).

[21]  Xinxing Li,et al.  A Cotton Disease Diagnosis Method Using a Combined Algorithm of Case-Based Reasoning and Fuzzy Logic , 2021, Comput. J..

[22]  Werner Nutt,et al.  Remote Method Invocation , 2009, Encyclopedia of Database Systems.

[23]  Jinfu Ni,et al.  Text-to-Speech Synthesis , 2019, Speech-to-Speech Translation.